Staff Applied Scientist, Financial Forecasting (Machine Learning)

Location: New York City or San Francisco, United States

Salary: $250,000–$330,000 annual base pay in San Francisco; compensation may be adjusted based on employee location. Total compensation may include equity, benefits, and bonus or variable pay.

Company

Vercel builds agentic infrastructure and developer products including Next.js, v0, and AI SDK.

What you will do

• Architect and own end-to-end consumption forecasting ML systems for compute, bandwidth, edge functions, storage, and emerging products.
• Design and productionize advanced time-series forecasting approaches, including deep learning, probabilistic and Bayesian methods, and hierarchical statistical-ML architectures.
• Develop multi-horizon forecasting systems with reconciliation across account, cohort, segment, and global aggregate levels.
• Build infrastructure for backtesting, monitoring, drift detection, retraining, and forecast explainability.
• Develop scenario simulation and causal inference frameworks for pricing, packaging, and product launch decisions.
• Partner with Finance, Infrastructure Engineering, Product, and GTM on planning, capacity, cost optimization, adoption, and usage modeling.

Requirements

• 8+ years of experience in machine learning, data science, or applied statistics, operating at staff or principal level.
• Deep hands-on expertise in advanced time-series forecasting, deep learning architectures, Bayesian or probabilistic modeling, and hierarchical reconciliation.
• Experience architecting and productionizing ML systems at scale, including training, serving, monitoring, and retraining infrastructure.
• Strong Python and SQL skills, experience with large-scale usage and billing datasets, and a strong foundation in causal inference and experimentation design.
• Experience setting technical direction, mentoring senior individual contributors, and partnering with Finance or executive leadership.
• Experience with cloud infrastructure, developer tools, or consumption-based revenue models.

Nice to have

• Experience in capacity planning or large-scale cost modeling.
• Experience with Snowflake, Delta Lake, dbt, Airflow, feature stores, or MLOps tooling.
• Prior technical leadership of a data science or ML team without formal management authority.

Culture & Benefits

• Competitive compensation package including equity.
• Inclusive healthcare package.
• Mentorship and support for attending professional events.
• Flexible time off.
• Provided equipment and a work-from-home budget.

Hiring process

• Applicants explain why they are a good fit for the role.
• Recruiters provide additional details about compensation during the hiring process.